Information about Test

  1. EEG analysis

    Dynamical system Chaos theory Artificial neural network Deep learning Convolutional neural network Recurrent neural network Machine learning Artificial intelligence

  2. Sensor fusion

    including: Central limit theorem Kalman filter Bayesian networks Dempster-Shafer Convolutional neural network Two example sensor fusion calculations are illustrated

  3. Conference on Neural Information Processing Systems

    in 1986 as NIPS at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and

  4. SqueezeNet

    Edgar (2017-03-02). "Introducing SqueezeDet: low power fully convolutional neural network framework for autonomous driving". The Intelligence of Information

  5. Jürgen Schmidhuber

    his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN

  6. Tsetlin machine

    and more efficient primitives compared to more ordinary artificial neural networks, but while the method may be faster it has a steep drop in signal-to-noise

  7. Graphical model

    Markov models, neural networks and newer models such as variable-order Markov models can be considered special cases of Bayesian networks. Naive Bayes classifier

  8. Bias–variance tradeoff

    when increasing the width of a neural network. This means that it is not necessary to control the size of a neural network to control variance. This does

  9. Kernel method

    (SVM) in the 1990s, when the SVM was found to be competitive with neural networks on tasks such as handwriting recognition. The kernel trick avoids the

  10. Bootstrap aggregating

    procedures" (Breiman, 1996), which include, for example, artificial neural networks, classification and regression trees, and subset selection in linear